Methods and apparatuses for AI-based ledger prediction

ABSTRACT

Apparatuses and methods for AI-based ledger prediction are provided. A processor is configured by instructions on a memory to receive and categorize a ledger file to a ledger type based on the data contained within the ledger file. The ledger data may contain information related to an insurance policy and investments made based on the payments into the policy over a period of time. The processor may be configured to utilize machine learning to generate a prediction of a value or values related to the ledger data, for example a retirement distribution amount.

FIELD OF THE INVENTION

The present invention generally relates to the field of machinelearning. In particular, the present invention is directed to methodsand apparatuses for AI-based ledger prediction.

BACKGROUND

Insurance policies such as life insurance are paid into over the courseof a customer's life and paid out relatively infrequently. This canresult in a relatively large sum of money that might otherwise be usedproductively such as for an investment. However, it is difficult topredict how a user may want or need to use such a sum of money and howbest to distribute or invest it.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for predictive ledger generation is provided.The apparatus includes a processor and a memory communicatively coupledwith the processor, the memory containing instructions stored thereon.The instructions configure the processor to receive a ledger filecontaining ledger data, classify the ledger file to a ledger type,analyze the ledger file to identify one or more trends in the ledgerdata, and store the ledger file to an immutable sequential listing.

In another aspect a method for predictive ledger generation is provided.The method includes the steps of receiving, by a processor, a ledgerfile containing ledger data, classifying, by the processor, the ledgerfile to a ledger type, analyzing, by the processor, the ledger file toidentify one or more trends in the ledger data, and storing, by theprocessor, the ledger file to an immutable sequential listing.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagram illustrating an apparatus for AI-based ledgerprediction;

FIG. 2 illustrates particular implementations of various steps of amethod for AI-based ledger prediction;

FIG. 3 is a diagram of an immutable sequential listing according to anembodiment of the invention;

FIG. 4 is a block diagram of a chatbot in accordance with an embodimentof the description;

FIG. 5 is a diagram of a machine learning module in accordance with anembodiment of the description;

FIG. 6 is a diagram illustrating a neural network in accordance with anembodiment of the description;

FIG. 7 is a diagram illustrating a node of a neural network inaccordance with an embodiment of the description; and

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof; and

FIG. 9 is an illustration of a segmentation and trendline fit to ledgerdata in accordance with an embodiment.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for predictive ledger generation and analysis usingmachine learning or artificial intelligence. In the insurance industry,particularly life insurance, a user will pay into a life insurancepolicy over the course of their lifetime and a pool of money will accrueto the insurance company. However, the money paid into the lifeinsurance policy is frequently underutilized as an investment ortax-savings vehicle. A challenge related to utilizing paid-in premiumsis predicting how an insurance ledger may grow or shrink over time aspremiums increase, distributions are needed due to death or retirement,or other considerations. The present invention provides methods andapparatuses to do so.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for AI-based ledger prediction is illustrated. Apparatus 100 includes acomputing device 104. Computing device 104 includes at least oneprocessor 108 and at least one memory 112 communicatively coupled to theprocessor 108. Processor may include, without limitation, any processordescribed in this disclosure. Computing device 104 may include a machinelearning module 116. Computing device 104 and/or processor 108 may becommunicatively coupled with a storage 120. Processor may be included ina computing device. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing device104 may include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented, as a non-limiting example, using a“shared nothing” architecture.

With continued reference to FIG. 1 , computing device 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Computing device 104 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

As used in this disclosure, “communicatively connected” means connectedby way of a connection, attachment or linkage between two or more relatawhich allows for reception and/or transmittance of informationtherebetween. For example, and without limitation, this connection maybe wired or wireless, direct or indirect, and between two or morecomponents, circuits, devices, systems, and the like, which allows forreception and/or transmittance of data and/or signal(s) therebetween.Data and/or signals therebetween may include, without limitation,electrical, electromagnetic, magnetic, video, audio, radio and microwavedata and/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure.

With continued reference to FIG. 1 , processor 108 may becommunicatively coupled with a storage 120. “Storage” is defined as anydevice capable of storing information. Storage 120 may be any datastorage in accordance with this disclosure including a hard drive, cloudstorage, distributed cloud storage, server, solid-state hard drive,magnetic tape storage, paper, written records, an object containing anindication of data or a datum, and the like. Storage 120 containsinformational contents 124. “Informational contents” are defined hereinare one or more datum elements that contain, store, or indicateinformation. Informational contents 124 may include any data capable ofbeing stored electronically. In some embodiments, storage 120 mayinclude a database. A database may be implemented, without limitation,as a relational database, a key-value retrieval database such as a NOSQLdatabase, or any other format or structure for use as a database that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Database may alternatively or additionallybe implemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Database mayinclude a plurality of data entries and/or records as described above.Data entries in a database may be flagged with or linked to one or moreadditional elements of information, which may be reflected in data entrycells and/or in linked tables such as tables related by one or moreindices in a relational database. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistently with this disclosure.

With continued reference to FIG. 1 , processor 108 is configured toreceive a ledger file 128 containing ledger data 132. A “ledger file,”as used in this disclosure, is defined as a file containing one orelements of information related to transactions and/or aspects ofinsurance processes. A ledger file may be a data object such as aspreadsheet, a comma-separated values (CSV) file, a computer file, aplaintext document, a database or database object, an image, a graph, amodel, a drawing, or any data object containing or storing digitalinformation. A ledger file may be a physical file (e.g. a piece ofpaper, a written record, a printed record, a physical printout, a paperphotocopy, and the like. “Ledger data,” as used herein, is defined asone or more elements of information contained in a ledger file. Ledgerdata 132 may be in any suitable form including a graph, a database, alist, a dictionary, a spreadsheet, a table, a matrix, a column ofnumbers, words, or characters, and the like. In an embodiment, a ledgerfile may be an insurance ledger file. In an embodiment, ledger data 132stored on ledger file 128 may contain information related to insurancepremium, cash surrender value, accumulation value, death benefit, loanamounts, deductible, covered objects or people, maximum coverage level,a risk outlay value, distribution from the policy, and the like. Ledgerdata 132 may contain user information 140. For example, ledger data 132may contain information related to identification of a user (such asname, date of birth, age, height, weight, sex, ethnicity, address,social security number, photographic information, and the like),information regarding a user's insurance status (e.g. existing policydata, covered items or people, coverage amounts, premium such as monthlypremium, deductible such as yearly deductible, death benefit, loanamounts, loan types, a date or dates related to insurance coverage, andthe like), one or more indications of external events that may haveaffected ledger data 132 (e.g. a car crash a user was in, anenvironmental event such as an earthquake, a flood, or a hailstorm, anunexpected medical event such as a heart attack or allergic reaction, aplanned distribution or initiation of retirement income, death of a useror associated person, and the like) and the like.

With continued reference to FIG. 1 , processor 108 is further configuredto classify the ledger file 128 to a ledger type. As used herein, a“ledger type” is a classification of ledger indicating a subject towhich the ledger relates or a subject about which the ledger containsinformation. As used herein, ledger type and ledger classification maybe used interchangeably. In an embodiment, a ledger type may be aninsurance ledger, a financial ledger, an investment ledger, a lifeinsurance premium ledger, a planning ledger (e.g. a ledger of plannedinvestments, distributions, withdrawals, premia, and the like), and thelike. Additionally or alternatively, classifying the ledger file 128 toa ledger type 136 may include classifying the data within the ledger toone or more data structures within the ledger, such as a tab. Forexample, a ledger may contain data in three separate portions of theledger file such as three separate pages, sheets, tabs, and the like.Classifying the ledger file 128 to a ledger type 136 may includeclassifying ledger data 132 within the ledger to one or more of theportions of the ledger file. For example, a ledger file 128 may includea tab with monthly premium deposits, a second tab with cumulative policyvalue measured each month, and a third tab with monthly return oninvestment. Processor 108 may classify the ledger data 132 by moving theledger data 132 into a portion of ledger file 128 in response to adetermination that some or all of ledger data 132 matches a data patternsuch as a range of values, a uniformity of values (e.g. monthly premiumdata may fit a pattern of each data element of the analyzed portion ofledger data 132 being the same for 12 entries and increases by 5% orless every 12th entry), a magnitude of values, or a trendline asdescribed below. In an embodiment, processor 108 may perform a curve fitto some or all of ledger data 132 and may classify the fitted ledgerdata 132 to a particular tab based on the determined fit. Processor 108may classify ledger file 128 based on the classification of ledger data132, for example by determining that the ledger data 132 matches one ormore categories that processor 108 is configured by instructionscontained on memory 112 to associate with a particular category. In anembodiment, processor 108 may determine that ledger data 132 contains anumber of categories associated with a particular ledger type. Forexample, ledger data 132 may contain the categories of monthly premiumand cumulative policy value and determine that ledger file 128 is of thelife insurance ledger type.

A ledger file 128 may include formatting and style functionalities. Aledger file 128 may allow for the addition of multiple predictiveledgers, for example by the inclusion of additional tabs or sheets. Aledger tab may include a spreadsheet of numbers and may include a titleand sub-title that a user may edit through user interface 144. A “userinterface,” as used in this disclosure, is a means by which the user anda computer system interact, including the use of input devices andsoftware. For example, a user may input into a user interface, socialmedia platforms they have accounts on and would like to retrieve userdata from. A user interface may include a graphical user interface(GUI), command line interface (CLI), menu-driven user interface, touchuser interface, voice user interface (VUI), form-based user interface,and the like. In an embodiment, user interface 144 may comprise aspeaker and/or microphone. In an embodiment, user interface 144 mayinclude a display (such as a light emitting diode (LED) display, liquidcrystal, quantum dot LED (QLED), organic LED (OLED), active-matrixorganic LED (AMOLED), Super AMOLED, and the like), touch screen, ordigital writing device. User interface 144 may comprise one or moremeans for receiving user input such as a keypad, keyboard, mouse,button, touchscreen (including shapes, icons, objects, images, prompts,colors, and the like displayed on a touchscreen which indicate a userintention or selection when touched), touchpad, knob, dial, slider,switch, or the like. User interface 144 may comprise one or more meansfor providing output such as a display, screen, speaker, vibrating motor(such as the type for vibrating smartphones), LED, light, buzzer, alarm,or the like. A user may select an individual cell or entire rows and/orcolumns to apply formatting or conditional formatting options. A userinterface 144 may include a chatbot to facilitate the understanding andinterpretation of the ledger data 132 analyzed by apparatus 100. A userinterface 144 may be compatible with publicly available softwarecollaboration tools.

With continued reference to FIG. 1 , a spreadsheet included in ledgerfile 128 may contain one or more cells. A “cell” as used herein is anelement of a spreadsheet. The ledger file 128 may include notes ornotations, user comments, and the like. The ledger file 128 may includea tab containing information in graphical or picture format, such as atab with one or more graphs. A tab of ledger file 128 may include avisualization or model. In an embodiment, a tab may have statisticalcalculations based on a first tab, such as mean, median, mode, standarddeviation, and similar statistical calculations of values contained inthe first tab. A user may edit data in a first tab through userinterface 144 and processor 108 may automatically update any ledger data132 in other tabs related to or associated with the edited data. In anembodiment, a plurality of tabs may contain different predictionsrelated to ledger data 132 in a first tab based on various assumptionsor changes to the ledger data 132 in the first tab. In an additionalembodiment, a user may add a comparison tab using the user interface 144that may prepopulate with data from another tab and shows a side-by-sidecomparison of at least two portions of ledger data 132 with anindication of a difference between the at least two portions. The usermay show or hide data columns as desired using user interface 144including data columns from other tabs or other ledger files entirely.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to classify a ledger file 128 to a ledger type 136 usingmachine learning. For example, processor 108, machine learning module116, or another device may receive or generate ledger classificationtraining data associating exemplary ledger data with ledger types.“Training data,” as used in this disclosure, is data containingcorrelations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, by labeling of a given element of data by a user aspertaining to a given ledger type, or the like. Multiple data entries intraining data may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data may be formatted and/or organizedby categories of data elements, for instance by associating dataelements with one or more descriptors corresponding to categories ofdata elements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

With continued reference to FIG. 1 , training data may include one ormore elements that are not categorized; that is, training data may notbe formatted or contain descriptors for some elements of data.Machine-learning algorithms and/or other processes may sort trainingdata according to one or more categorizations using, for instance,natural language processing algorithms, tokenization, detection ofcorrelated values in raw data and the like; categories may be generatedusing correlation and/or other processing algorithms. As a non-limitingexample, in a corpus of text, phrases making up a number “n” of compoundwords, such as nouns modified by other nouns, may be identifiedaccording to a statistically significant prevalence of n-gramscontaining such words in a particular order; such an n-gram may becategorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by processor 108, machine learning module 116, and/oranother device may correlate any input data as described in thisdisclosure to any output data as described in this disclosure. Forexample, processor 108 may classify a ledger file 128 to a ledger type136 using machine learning by first creating or receiving ledgerclassification training data. Ledger classification training data maycorrelate ledger data 132 within a ledger file 128 to a particular classof ledger. For example, ledger classification training data maycorrelate ledger data corresponding to percentage monthly return oninvestment to an investment ledger classification type.

With continued reference to FIG. 1 , a classifier may be configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, found to be close under a distancemetric such as a norm, or the like. Machine-learning module 116 maygenerate a classifier using a classification algorithm, defined as aprocess whereby a computing device and/or any module and/or componentoperating thereon derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. As anon-limiting example, processor 108, machine learning module 116, oranother device may classify elements of ledger classification trainingdata into different ledger types such as an investment ledger, a lifeinsurance ledger, a monthly premium ledger, a payout ledger, and thelike. Processor 108, machine learning module 116, or another device mayfurther classify sub-types such as sub-categories of payout ledger, suchas cash value, surrender value, accumulation value, death benefit, andthe like. Processor 108, machine learning module 116, or another devicemay be programed by instructions contained on memory 112 to classifyelements of training data based on similarity of values (e.g. a variancebelow a threshold, standard deviation below a threshold, differencebetween maximum and minimum values below a threshold), an existinglabeling within the data (for instance the data may be labeled by ahuman, or may be labeled by processor 108, or may be labeled by anotherdevice or process), or any suitable selection criteria. Processor 108may retrieve data corresponding to ledger classification and existingimprovements to ledger classification from storage 120, memory 112,machine learning module 116, or any suitable storage source.

With continued reference to FIG. 1 , processor 108 may be configured togenerate a classifier using a Naive Bayes classification algorithm.Naive Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naive Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naive Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naive Bayes algorithm may be generatedby first transforming training data into a frequency table. Processor108 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Processor 108 mayutilize a naive Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naive Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1 , processor 108 may be configured togenerate a classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm:

${l = \sqrt{{\sum}_{i = 0}^{n}a_{i}^{2}}},$where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance, be advantageous wherecases represented in training data are represented by differentquantities of samples, which may result in proportionally equivalentvectors with divergent values.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to train a ledger classification machine learning model usingthe ledger classification training data. In a non-limiting embodiment,ledger classification training data is submitted to a machine-learningmodel, which generates a trained ledger classification machine learningmodel based on the correlated relationship or relationships. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model may be generated by creating an artificial neuralnetwork, such as a convolutional neural network comprising an inputlayer of nodes, one or more intermediate layers, and an output layer ofnodes. Connections between nodes may be created via the process of“training” the network, in which elements from the ledger classificationtraining data set are applied to the input nodes, a suitable trainingalgorithm (such as Levenberg-Marquardt, conjugate gradient, simulatedannealing, or other algorithms) is then used to adjust the connectionsand weights between nodes in adjacent layers of the neural network toproduce the desired ledger classification values at the output nodes.This process is sometimes referred to as deep learning.

With continued reference to FIG. 1 , processor 108 may be configured toutilize a trained ledger classification machine learning model togenerate a ledger classification. For example, a trained ledgerclassification machine learning model may receive all of or a portion ofledger file 128, or ledger data 132, as an input. The ledgerclassification machine learning model may then output a ledger fileclassification based on the data types, the organization of data withinthe ledger file 128, and the correlations indicated in the ledgerclassification training data. For example, a ledger file 128 may containa graph of data points containing an X and Y axis where the X axis islabeled “monthly premium payments”. Ledger classification machinelearning model may receive the labeled data after having been trained torecognize the word “premium” in the X axis label and classify the ledgerfile 128 as a premium payment ledger file. In an additional oralternative example, ledger classification machine learning model mayreceive a ledger file 128 containing three separate portions. A ledgerclassification machine learning model may correlate the number ofseparate portions with a particular ledger file classification and mayoutput the corresponding ledger file classification. In an additional oralternative example, a ledger classification machine learning model maybe updated using each ledger file it classifies. For example, a ledgerfile 128 may be classified according to user and the machine learningmodel updated so that future ledger files have a continually corpus ofledger files to use for classification. Outputs may include any of theabove listed types of ledger files including insurance ledger files,premium ledger files, cash value ledger files, cash surrender valueledger files, accumulation value ledger files, death benefit ledgerfiles, loan amount ledger files, policy distribution ledger files,investment return ledger files, health insurance ledger files, homeinsurance ledger files, auto insurance ledger files, life insuranceledger files, boat insurance ledger files, recreational vehicle (RV)insurance files, and the like.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to classify a ledger file 128 based on a user or user type.For example, a ledger file 128 may include a customer name. Memory 112may contain instructions configuring processor 108 to scan the ledgerfile 128 and extract the user name. Processor 108 may be furtherconfigured to compare the user name with informational contents 124 ofstorage 120 containing user names and their associated ledger file typesby receiving the user name from storage 120. Processor 108 may thenclassify the ledger file according to the indicated ledger file type. Inan alternative embodiment, processor 108 may utilize a ledgerclassification machine learning model trained on user data to classifythe ledger file 128. For example, ledger classification machine learningmodel may be trained using ledger classification training datacorrelating user parameters such as age, occupation, location, healthhistory, and the like to ledger file classifications. Processor 108 mayaccordingly classify a ledger file 128 by inputting one or more userparameters extracted from a ledger file 128 to the trained ledgerclassification machine learning model and receive as output a ledgerfile classification based on the one or more user parameters.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to classify ledger file 128 based on a pecuniary parameter.“Pecuniary parameter,” as used herein, is defined as an element ofinformation related to money. In an embodiment, a pecuniary parametermay be a monthly premium payment, a monthly or annual return oninvestment, a death benefit, a distribution amount, a cash value amount,a cash surrender amount, and the like. For example, memory 112 maycontain instructions configuring processor 108 to extract a data labelfrom ledger file 128 or ledger data 132 by receiving ledger data 132 andcomparing received data with a label keyword such as “premium”. Thekeywords used by processor 108 may be linked to a defined ledger class,for example by a person manually associating keywords with a definedledger class or by a computing device such as computing device 104programmed to create an association between a keyword and a definedledger class, for instance by extracting words contained in ledger data132. In the above example, processor 108 may identify the keywords“death” and “benefit” and classify the ledger file 128 based on theassociation between those keywords and the ledger class. In anembodiment, the pecuniary parameter may correspond to (e.g. be relatedto, linked to, or associated with) both an investment parameter and aninsurance parameter. “Investment parameter,” as used herein, is definedas an element of information related to an investment. “Insuranceparameter,” as used herein, is defined as an element of informationrelated to insurance. For example, the pecuniary parameter used toclassify ledger file 128 may be a distribution amount when a userassociated with the ledger file 128 reaches 65 years of age. Such adistribution amount may correspond to an investment of life insurancepolicy monthly premia and thereby be linked to an investment parameterof total invested amount (e.g. because processor 108 may be configuredby instructions contained on memory 112 determine that distributionamount may not exceed total invested amount) and an insurance parameterof monthly premium (e.g. because processor 108 may be configured byinstructions contained on memory 112 to determine distribution amountscales linearly with deposited monthly premium).

With continued reference to FIG. 1 , processor 108 is further configuredto analyze the ledger file 128 to identify one or more trends in theledger data 132. A “trend” as used herein is defined as an identifiabletendency in a plurality of data elements. For example, if given fivedata elements with values of 5, 6, 7, 8, and 9, in that order, the dataelements could be described as having a linear increasing trend; eachsuccessive data element is one higher than the previous data element.Memory 112 may configure processor 108 to identify one or more trends inthe ledger data 132 using a curve fitting process such as least squaresregression, polynomial regression, polynomial interpolation, and/orsplining to determine a curve such as a linear or exponential curve thatbest fits the data. Processor 108 may use a gradient descent algorithmto determine a curve to fit to ledger data 132. For example, a gradientdescent algorithm used by processor 108 to determine a curve to fit toledger data 132 may include a stochastic gradient descent algorithm,which may include a method that iteratively optimizes an objectivefunction, such as an objective function representing a statisticalestimation of relationships between terms, including relationshipsbetween input terms and output terms, in the form of a sum ofrelationships to be estimated. Ledger data 132 may be organized suchthat each data element has a specified order and the least squaresregression process that memory 112 may configure processor 108 to usemay order the data according to the specified order. For example,elements of ledger data 132 may be indexed. Identified trends mayinclude a linear trend, a parabolic trend, an exponential trend, and thelike. Identified trends may have associated curves; for example, aparabolic trend may be best fit by an associated parabolic curve.Processor 108 may be further configured to identify one or morelocations of the trend where the trend “breaks down;” in other wordswhere the least squares fit between data points and a fitted curvebecomes higher than a threshold.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to segment the ledger data 132 into one or more trendsegments based on the identified one or more trends. A “trend segment”as used herein is defined as a segment of ledger data 132 thatcorresponds to a trend. For example, a trend segment may include alldata points within a defined threshold least squares distance from atrendline. Memory 112 may configure processor 108 with a definedthreshold, or processor 108 may be configured by memory 112 to determinea threshold based on a percentage of each data element value (e.g. aleast squares distance between a trendline and a data element must bewithin ±5% of the data element value), or the like. In an additional oralternative embodiment, processor 108 may be configured to segment theledger data 132 into one or more trend segments based on an indicationin ledger data 132 indicating where ledger data 132 should be segmented.For example, ledger data 132 may contain labeled data indicating twoindex numbers defining the start and end of a segment. Ledger data 132may contain a specified number of segments, or memory 112 may containinstructions configuring processor 108 to identify index numbersdefining specified segments, for example by using a regression analysisas described herein. The identified index numbers may correspond tolocations where all data element values between two index numbers arewithin a threshold least squares distance of a trend line. Processor 108may be configured by instructions on memory 112 to determinecoefficients for curve fitting methods such as splining or polynomialregression iteratively or recursively, for example by using an algorithmsuch as a gradient descent algorithm. For example, processor 108 maydetermine coefficients for a fitted trendline by utilizing an iterativeoptimization algorithm such as gradient descent that uses thedifferential of the trendline function to find the fastest decrease inthe gradient. This process may be iterated until a desired local minimumis reached, which corresponds to coefficients for an optimally fittedtrendline for the ledger data 132. Processor 108 may then use thesecoefficients to generate a trendline such as a segment or predictivetrendline outlined below. Processor 108 may further store the determinedcoefficients in immutable sequential listing 148 in accordance with thebelow description.

With continued reference to FIG. 1 , the processor 108 may be configuredto segment the ledger data 132 into one or more trend segments byidentifying one or more external events influencing the ledger data 132.For example, ledger data 132 may contain a label indicating an indexnumber or index numbers indicating data that has been affected by anexternal event such as a distribution, a health event resulting in anincrease in monthly premium, or the like. Ledger data 132 may have anexpected profile and identifying the one or more external events andsegmenting the ledger data 132 into one or more segments based on theidentified external events may include determining that a trendlinefitted to ledger data 132 fails to follow the expected profile after acertain index value, between two index values, or until a certain indexvalue. A determination that a trendline fails to follow an expectedprofile may include a determination that a least squares distancebetween a data element and a trendline exceeds a predefined threshold(for instance predefined as programmed in instructions stored on memory112). In an alternative embodiment, ledger file 128 may indicateexternal events by providing data already separated into tabs or otherdelineators within the ledger file 128. As used herein, a “delineator”is a data element indicating a separation of data. For example, a firsttab may indicate a segment of data from before a user had a healthepisode (such as a heart attack), a second tab may indicate a segment ofdata from during the user's health episode, and the third tab mayindicate a segment of data from after the user's health episode. Memory112 may configure processor 108 to treat tabs as delineatorsautomatically and thereby segment data associated with each tab. In analternative embodiment, processor 108 may be configured by instructionscontained on memory 112 to identify the slope of a trendline at eachdata element. Processor 108 may further identify an external event basedon a sudden increase or decrease in trendline slope. For example,processor 108 may determine the slope at a given index location bydetermining the slope between index i_(x−1), i_(x), and i_(x+i). Forexample, processor 108 may take the average of (i_(x)−i_(x−1)) and(i_(x)−i_(x+1)) to determine the effective slope at i_(x). Processor 108may repeat this for each data point as applicable (not counting theendpoints) and may define an external event as data points on eitherside of or between indices where the slope changes more than a thresholdamount. Processor 108 may change the data in a delineated column and thedata depending from the delineated column may change according to arelationship specified in the ledger data 132. This can work in eitherdirection. For example, processor 108 may change cash surrender valuewhich would cause accumulation values to change, or may changeaccumulation values and cash surrender values change. In contrast, adependency may be a one-way dependency. For example, processor 108 maybe configured to change or receive indication of a user change throughuser interface 144 of ledger data 132 in an editable column, which mayresult in ledger data 132 in a dependent data column changing. However,changing the dependent column may not result in the editable columnchanging. For example, increasing premium may increase cash value, butincreases in cash value from good investment performance may notcorrespondingly increase premiums. Independent and dependent data may bespecified based on instructions contained on memory 112 or by user inputreceived from user interface 144. For example, changing the dependentdata related to death benefit will not change independent data relatedto the cash value or premium, but changing the cash value or premiumwill change the death benefit.

With continued reference to FIG. 1 , processor 108 may be configured tosegment the ledger data 132 by removing data indicative of an externalevent. For example, processor 108 may be configured to determine one ormore segments corresponding to an external event as outlined above, andin response to the determination, remove the data corresponding to theexternal event segments such as sudden upturns or downturns of theledger data 132. Processor 108 may then be configured by instructionscontained on memory 112 to perform an interpolation process as is knownin the art, rejoin or splice the remaining segments, or fill in theremoved data with smoothed data. Processor 108 may then generate a newtrendline fitting the remaining data once the data associated with theexternal event has been removed. Rejoining or splicing the remainingsegments may be performed by processor 108 as a function of the numberof data elements removed in association with an external event. Forexample, processor 108 may be configured by instructions contained onmemory 112 to segment the ledger data 132 and generate a trendline whenthe number of removed data elements associated with an external eventare below a threshold number or percentage of the data, or when thechange in slope between the data before the event and the data after theevent is below a threshold.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to minimize an effect of the one or more external events onthe ledger data 132. As used herein, “minimize an effect” is defined asreducing a least squares difference between at least one data element ofledger data 132 and a trendline after the ledger data 132 has beenaltered compared to the same trendline prior to alteration of the ledgerdata 132. For example, processor 108 may receive ledger data 132 andgenerate a trendline minimizing the least squares regression. Processor108 may identify that the data has been affected by an external eventand may be configured by instructions stored in memory 112 to determinethat the effect of the external event should be minimized. In responseto the determination, processor 108 may remove the ledger data 132associated with the external event, may smooth the ledger data 132associated with the external event (for instance by replacing theexternal event data with an average of the affected values, byinterpolating the non-event data indexed adjacent to the external eventdata, or the like), or the like. Once the higher variance external eventdata has been smoothed or removed, processor 108 may generate a secondtrendline for the smoothed ledger data 132, for example using techniquesdescribed above. In smoothing or removing the external event data,processor 108 may be configured to reduce the variance in the overalldata with respect to the least squares distance of the adapted ledgerdata 132, thus minimizing the overall effect of the external event.

With continued reference to FIG. 1 , the processor 108 may be configuredto fit one or more segment trendlines to each of the one or more trendsegments. A “segment trendline” as used herein is defined as a trendlinefitted to a determined segment. For example, processor 108 may beconfigured to determine segments based on their location in a tab of aledger file 128. In an alternative embodiment, ledger file 128 maycontain labeled data indicating indices corresponding to the start andend values of one or more segments. Processor 108 may determine segmentsin any suitable way, including those methods outlined above. Processor108 may be configured to fit an individual trendline for each segmentidentified in ledger data 132. Exemplary trendlines determined byprocessor 108 include linear, exponential, parabolic, polynomial, power,moving average, or any suitable trendline. Ledger data 132 being fit mayinclude data related to surrender values, accumulation values, income,death benefit, premium, cash surrender value, loan amounts, policydistributions, return on investment, and the like.

With continued reference to FIG. 1 , processor 108 may be configured tofit one or more segment trendlines to each of the one or more trendsegments using machine learning. For example, processor 108, machinelearning module 116, or another device may receive or generate trendlinefit training data correlating exemplary ledger data or ledger dataparameters with exemplary fitted trendlines. “Ledger data parameters,”as used herein, are defined as one or more elements of ledger datadescribing, defining, or included in the ledger data. In an additionalor alternative embodiment, trendline fit training data may correlatepatterns of segments, such as trendline fit training data that maycorrelate exemplary ledger data with data segments that match a wholedata set when taken together in a specified order. For example, ledgerdata 132 may include a first section of linear data and a second sectionof exponential data. Trendline fit training data may correlate a ledgerdata parameter such as age group with a data pattern or data segmentpattern, such as expected policy cash value for a user aged 65 or olderbeing correlated with an exponential growth segment followed by a suddendrop in value segment, e.g. the user pays into the policy throughouttheir working lives and the policy grows in investment value(constituting the exponential growth), then takes a distribution fromthe policy when they reach retirement age at 65 (constituting the suddendrop in value segment).

With continued reference to FIG. 1 , processor 108 may be configured togenerate one or more additional homogeneous segment trendlines, whichmay then optionally be used to train a trendline fit machine learningmodel. In an embodiment, processor 108 may receive ledger data 132containing a dataset such as monthly cash value for a 20 year old policy(which would have 240 data points). In the event that processor 108 hasnot received more than a threshold number of similarly classifieddatasets (for example, 100 similarly classified datasets), the processor108 may not be able to generate a trendline of sufficiently accurate fitbased on previously received ledger data. To address this, processor 108may create one or more data clones of ledger data 132 by propagating asegment trendline generated from ledger data 132 using a plurality ofcorresponding coefficients. In an embodiment, processor 108 isconfigured by instructions contained on memory 112 to generateadditional segment trendlines by multiplying the segment trendlinegenerated from ledger data 132 by each coefficient. In an embodiment,memory 112 may contain instructions with a predetermined number ofcoefficients, for example 100 coefficients starting with 1.01 andincreasing by 0.01 up to 2.00. Processor 108 may then multiply eachtrendline by the coefficient (in other words, multiplying each datapoint element defining a segment trendline or trendline formuladependent variable) in order to create a set of 100 trendlines. In anembodiment, processor 108 may then apply this uniform set of segmenttrendlines to train a trendline fit machine learning model. In anembodiment, a suitable coefficient may be between 0.5 and 1.5, 1.0 and2.0, 0.01 and 100, or any suitable range. In an embodiment, the numberof coefficients may be 2 or more, 10 or more, 100 or more, 200 or more,300 or more, 400 or more, 500 or more, 1000 or more, or any suitablenumber.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to train a trendline fit machine learning model oncetrendline fit training data is generated or received. In a non-limitingembodiment, trendline fit training data is submitted to amachine-learning model, which generates a trained ledger classificationmachine learning model based on the correlated relationship orrelationships. For instance, and without limitation, a linear regressionmodel, generated using a linear regression algorithm, may compute alinear combination of input data using coefficients derived duringmachine-learning processes to calculate an output datum. As a furthernon-limiting example, a trendline fit machine-learning model may begenerated by creating an artificial neural network, such as aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from the trendline fit training data set are appliedto the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredsegment trendline values at the output nodes. This process is sometimesreferred to as deep learning. In an embodiment, processor 108 may befurther configured to update trendline fit training data with ledgerdata 132 received from or relating to a user and retrain the trendlinefit machine learning model with the updated trendline fit training datasuch that the trendline fit machine learning model is regularly updated.

With continued reference to FIG. 1 , once trained, processor 108 may beconfigured to utilize a trained trendline fit machine learning model togenerate one or more trendlines. In an embodiment, processor 108 may beconfigured to output one or more segment trendlines by inputting trendsegments into the trained trendline fit machine learning model andreceiving the one or more segment trendlines as outputs from the trainedtrendline fit machine learning model. The segment trendlines may beapplied to the ledger data 132 and displayed to a user or stored viastorage 120 or on a immutable sequential listing as described below. Thesegment trendlines may include labels, such as a text label indicatingwhat kind of curve has been fit (e.g. a label indicating that the curveis an exponential curve), what class of data the fitted portion ofledger data 132 represents (e.g. a label indicating that the data isinsurance premium data, the data is a cash surrender value, the data isretirement income, etc.). A “label” as used herein, is defined as anindication of information regarding a trendline in a visual format. Theprocessor 108 may be further configured to display predictions based onone or more predictive trendlines, such as those outlined below. Aprediction may be a visual or audio indication of a future value relatedto ledger data 132. For example, a prediction may include a predictivevalue (e.g. an indication of a future distribution amount, a predictedfuture investment amount, a projected future monthly premium, aprojected cash surrender value, a projected loan amount, and the like)and/or a date associated with a prediction (e.g. a date of retirement, adate of disbursal of investment, a date of policy termination, a date ofpredicted investment value, and the like). Processor 108 may beconfigured by instructions stored on memory 112 to display both apredictive value and a date at which a predictive value occurs, such asa predicted retirement date and a predicted retirement distributionamount. A trendline fit machine learning model may receive a collectionof data such as ledger data 132 and may determine the best fitting typeof trendline as well as the trendline parameters such as trendlineequation, trendline slope (for a linear curve), trendline exponents (fora power or exponential curve), and the like, based on the correlationsidentified in the trendline fit training data.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to generate at least one predictive trendline based on thesegment trendlines. A “predictive trendline” as used herein is definedas a trendline used to indicate, predict, or otherwise inform futuredata. For example, processor 108 may use an extrapolation technique suchas linear extrapolation, exponential extrapolation, power lawextrapolation, parabolic extrapolation, and the like. A type ofextrapolation may be determined based on the trendline fit for theselected segment. For example, if a predictive trendline is to begenerated based on a segment indicating cumulative value of depositedpremium, a segment trendline fitted to the data may be linear if themonthly premium is constant. Processor 108 may be configured to performan extrapolation of the trendline, which would be a linear extrapolationsince the corresponding trendline segment being extrapolated is linear.This would generate a predictive trendline indicating that thecumulative value of deposited premium should grow linearly and provide auser with related information of what a future value of their lifeinsurance policy might be worth after a selected period of time such asfive years. Ledger data 132 for which at least one predictive trendlineis generated by processor 108 may include data related to surrendervalues, accumulation values, income, death benefit, premium, cashsurrender value, loan amounts, policy distributions, return oninvestment, and the like.

With continued reference to FIG. 1 , processor 108 may be furtherconfigured to optimize the at least one predictive trendline based oninput received from a user through a user interface. “Optimize” as usedherein is defined as improved, designed for a particular purpose,maximizing a desired characteristic, or tending to improve a desiredcharacteristic. For example, a predictive trendline may be generatedusing a predictive trendline machine learning model using the techniquesdescribed above with respect to generating a trendline using machinelearning. A user interface may include one or more inputs as outlinedabove, including, for example, an icon labeled “Apply AI.” A user maypress the “Apply AI” button or otherwise input instructions to theprocessor 108 to optimize the at least one predictive trendline forledger data 132. For example the Apply AI button may instruct theprocessor 108 to determine additional trendline fit training data hadbecome available since the last time the trendline fit machine learningmodel was trained, generate new trendline fit training dataincorporating the additional trendline fit training data, train atrendline fit machine learning model, and input the ledger data 132 intothe updated trendline fit machine learning model and receive anoptimized trendline fit as an output. This functionality may provide auser with instant feedback and projections after uploading ledger data132 based on premium, cash surrender value, accumulation value, deathbenefit, loan amounts, distribution from the policy, and other pecuniarydata. Processor 108 may further display one or more indications ofchanges made to one or more predictive trendlines based on the inputreceived from a user. For example, processor 108 may display a firstpredictive trendline in a gray color to indicate that the firstpredictive trendline is not the newest and most updated trendline andmay display a second predictive trendline in a bright green color toindicate that the second predictive trendline is the most up to datetrendline. In an embodiment, processor 108 may display labels with someor all predictive trendlines. For example, processor 108 may display atext label indicating a second predictive trendline as “UpdatedPredictive Trendline,” for example by displaying the aforementioned textinside a rectangular box with a leader line connecting the rectangularbox and the predictive trendline. Processor 108 may display theindication adjacent to an end of one or more predictive trendlines suchthat the proximity of the indication to the predictive trendline informsthe user which trendline the indication refers to.

With continued reference to FIG. 1 , processor 108 is further configuredto store the ledger file 128 to an immutable sequential listing 148. An“immutable sequential listing,” as used in this disclosure, is a datastructure that places data entries in a fixed sequential arrangement,such as a temporal sequence of entries and/or blocks thereof, where thesequential arrangement, once established, cannot be altered orreordered. An immutable sequential listing may be, include and/orimplement an immutable ledger, where data entries that have been postedto the immutable sequential listing cannot be altered. Processor 108 maycryptographically hash ledger file 128 and store it on the immutablesequential listing 148. The immutable sequential listing 148 may bedistributed or decentralized by storing multiple copies of the identicalimmutable sequential listing 148 in a plurality of storage locations. Inan alternative embodiment, the immutable sequential listing 148 may bestored only in informational contents 124 of storage 120. Processor 108may store ledger file 128 on immutable sequential listing 148 in orderto increase confidence that the data stored thereon will be secure bycryptographically storing the ledger file 128 as well as optionallyproviding distributed storage.

With continued reference to FIG. 1 , processor 108 may be configured toselect one or more elements of ledger data 132 to store to the immutablesequential listing 148 based on the ledger type 136, the one or moretrends, and/or the one or more labels or indications associated withtrendlines. For example, ledger data 132 may include comparative datafrom a plurality of ledgers. The ledger file 128 may be accordinglyclassified as a comparative data ledger. Processor 108 may be configuredby instructions contained on memory 112 to store only the elements ofledger data 132 original to the ledger file 128, i.e. not to store anyelements of ledger data 132 that were added to ledger file 128 forcomparative purposes. Processor 108 may be further configured to storeledger data 132 to the immutable sequential listing 148 if the ledgerdata 132 represents a predetermined type of information, such aspremium, cash surrender value, accumulation value, death benefit, loanamounts, distribution from the policy, and other pecuniary data.Processor 108 may be further configured to select one or more elementsof ledger data 132 based on a ledger type 136 associated with a user.For example, processor 108 may be configured to store one or moreelements of the ledger data 132 to the immutable sequential listing 148based on a determination that the user is over the age of 30, that theuser has been with an insurance company for more than a predeterminedlength of time such as 2 years, or the like. In an additional oralternative embodiment, processor 108 may be configured to store aportion or all of ledger data 132 to immutable sequential listing 148based on labels identifying the portion or all of ledger data 132 orlabels identifying one or more fitted trendlines. For example,instructions contained on memory 112 may configure processor 108 toidentify one or more labels associated with a portion or all of ledgerdata 132 and/or one or more segment and/or predictive trendlinesassociated with ledger data 132. Processor 108 may then be configured byinstructions contained on memory 112 to match the one or more labels orelements of the labels (such as text within the labels, a label colorassociated by instructions contained on memory 112 with a category, alabel shape associated by instructions contained on memory 112 with acategory, or the like) to a ledger type. Upon such a determination ofmatching, processor 108 may be further configured to store some or allof ledger data 132 to immutable sequential listing 148.

Referring now to FIG. 2 , a method 200 for predictive ledger generationis described. Method 200 comprises the steps of: 205 receiving, by aprocessor, a ledger file containing ledger data; step 210 classifying,by the processor, the ledger file to a ledger type; step 215 analyzing,by the processor, the ledger file to identify one or more trends in theledger data; and storing, by the processor, the ledger file to animmutable sequential listing.

Still referring to FIG. 2 , step 205 comprises receiving, by aprocessor, a ledger file containing ledger data. This step may beperformed in accordance with corresponding steps performed by apparatus100 with respect to FIG. 1 .

Still referring to FIG. 2 , step 210 comprises classifying, by theprocessor, the ledger file to a ledger type. This step may be performedin accordance with corresponding steps performed by apparatus 100 withrespect to FIG. 1 .

Still referring to FIG. 2 , step 215 comprises analyzing, by theprocessor, the ledger file to identify one or more trends in the ledgerdata. This step may be performed in accordance with corresponding stepsperformed by apparatus 100 with respect to FIG. 1 .

Still referring to FIG. 2 , step 220 storing, by the processor, theledger file to an immutable sequential listing. This step may beperformed in accordance with corresponding steps performed by apparatus100 with respect to FIG. 1 .

Referring now to FIG. 3 , an exemplary embodiment of an immutablesequential listing 300 is illustrated. Data elements are listing inimmutable sequential listing 300; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertions.In one embodiment, a digitally signed assertion 304 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 304. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 304register is transferring that item to the owner of an address. Adigitally signed assertion 304 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 3 , a digitally signed assertion 304 maydescribe a transfer of virtual currency, such as crypto-currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g. a ride share vehicle or anyother asset. A digitally signed assertion 304 may describe the transferof a physical good; for instance, a digitally signed assertion 304 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 304 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 3 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 304. In some embodiments, addressis linked to a public key, the corresponding private key of which isowned by the recipient of a digitally signed assertion 304. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 304 may record a subsequent adigitally signed assertion 304 transferring some or all of the valuetransferred in the first a digitally signed assertion 304 to a newaddress in the same manner. A digitally signed assertion 304 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 304 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 3 , immutable sequentiallisting 300 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 300 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 3 , immutable sequential listing 300 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 300 may organize digitally signedassertions 304 into sub-listings 308 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 304 within a sub-listing 308 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 308and placing the sub-listings 308 in chronological order. The immutablesequential listing 300 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif, or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger, but maynot allow any users to alter at least a posted content that have beenadded to the ledger. In some embodiments, ledger is cryptographicallysecured; in one embodiment, a ledger is cryptographically secured whereeach link in the chain contains encrypted or hashed information thatmakes it practically infeasible to alter the ledger without betrayingthat alteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 300 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 3 , immutablesequential listing 300, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 300 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 300 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 300 that records one or morenew at least a posted content in a data item known as a sub-listing 308or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 308 may becreated in a way that places the sub-listings 308 in chronological orderand link each sub-listing 308 to a previous sub-listing 308 in thechronological order so that any computing device may traverse thesub-listings 308 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 308 maybe required to contain a cryptographic hash describing the previoussub-listing 308. In some embodiments, the block chain contains a singlefirst sub-listing 308 sometimes known as a “genesis block.”

Still referring to FIG. 3 , the creation of a new sub-listing 308 may becomputationally expensive; for instance, the creation of a newsub-listing 308 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 300 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 308 takes less time for a given set ofcomputing devices to produce the sub-listing 308 protocol may adjust thealgorithm to produce the next sub-listing 308 so that it will requiremore steps; where one sub-listing 308 takes more time for a given set ofcomputing devices to produce the sub-listing 308 protocol may adjust thealgorithm to produce the next sub-listing 308 so that it will requirefewer steps. As an example, protocol may require a new sub-listing 308to contain a cryptographic hash describing its contents; thecryptographic hash may be required to satisfy a mathematical condition,achieved by having the sub-listing 308 contain a number, called a nonce,whose value is determined after the fact by the discovery of the hashthat satisfies the mathematical condition. Continuing the example, theprotocol may be able to adjust the mathematical condition so that thediscovery of the hash describing a sub-listing 308 and satisfying themathematical condition requires more or less steps, depending on theoutcome of the previous hashing attempt. Mathematical condition, as anexample, might be that the hash contains a certain number of leadingzeros and a hashing algorithm that requires more steps to find a hashcontaining a greater number of leading zeros, and fewer steps to find ahash containing a lesser number of leading zeros. In some embodiments,production of a new sub-listing 308 according to the protocol is knownas “mining.” The creation of a new sub-listing 308 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 3 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 308. The incentive may befinancial; for instance, successfully mining a new sub-listing 308 mayresult in the person or entity that mines the sub-listing 308 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 308 Each sub-listing 308 createdin immutable sequential listing 300 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 308.

With continued reference to FIG. 3 , where two entities simultaneouslycreate new sub-listings 308, immutable sequential listing 300 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 300 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 308 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 308 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 300branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 300.

Still referring to FIG. 3 , additional data linked to at least a postedcontent may be incorporated in sub-listings 308 in the immutablesequential listing 300; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 300. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 3 , in some embodiments, virtualcurrency is traded as a crypto-currency. In one embodiment, acrypto-currency is a digital, currency such as Bitcoins, Peercoins,Namecoins, and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto-currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto-currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 308 in a block chain computationallychallenging; the incentive for producing sub-listings 308 may includethe grant of new crypto-currency to the miners. Quantities ofcrypto-currency may be exchanged using at least a posted content asdescribed above.

Referring to FIG. 4 , a chatbot system 400 is schematically illustrated.According to some embodiments, a user interface 404 may be communicativewith a computing device 408 that is configured to operate a chatbot. Insome cases, user interface 404 may be local to computing device 408.Alternatively or additionally, in some cases, user interface 404 mayremote to computing device 408 and communicative with the computingdevice 408, by way of one or more networks, such as without limitationthe internet. Alternatively or additionally, user interface 404 maycommunicate with user device 408 using telephonic devices and networks,such as without limitation fax machines, short message service (SMS), ormultimedia message service (MMS). Commonly, user interface 404communicates with computing device 408 using text-based communication,for example without limitation using a character encoding protocol, suchas American Standard for Information Interchange (ASCII). Typically, auser interface 404 conversationally interfaces a chatbot, by way of atleast a submission 412, from the user interface 408 to the chatbot, anda response 416, from the chatbot to the user interface 404. In manycases, one or both of submission 412 and response 416 are text-basedcommunication. Alternatively or additionally, in some cases, one or bothof submission 412 and response 416 are audio-based communication.

Continuing in reference to FIG. 4 , a submission 412 once received bycomputing device 408 operating a chatbot, may be processed by aprocessor 420. In some embodiments, processor 420 processes a submission412 using one or more of keyword recognition, pattern matching, andnatural language processing. In some embodiments, processor employsreal-time learning with evolutionary algorithms. In some cases,processor 420 may retrieve a pre-prepared response from at least astorage component 424, based upon submission 412. Alternatively oradditionally, in some embodiments, processor 420 communicates a response416 without first receiving a submission 412, thereby initiatingconversation. In some cases, processor 420 communicates an inquiry touser interface 404; and the processor is configured to process an answerto the inquiry in a following submission 412 from the user interface404. In some cases, an answer to an inquiry present within a submission412 from a user device 404 may be used by computing device 104 as aninput.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 412;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , training data 504 may be in accordance withtraining data with reference to FIG. 1 .

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers.

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 520 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs as described in this disclosure as inputs, outputs asdescribed in this disclosure as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminant analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized trees, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 6 , an exemplary embodiment of neural network 600is illustrated. A neural network 600 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 604, one or more intermediate layers 608, and an output layer ofnodes 612. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.” Asa further non-limiting example, a neural network may include aconvolutional neural network comprising an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. A “convolutionalneural network,” as used in this disclosure, is a neural network inwhich at least one hidden layer is a convolutional layer that convolvesinputs to that layer with a subset of inputs known as a “kernel,” alongwith one or more additional layers such as pooling layers, fullyconnected layers, and the like.

Referring now to FIG. 7 , an exemplary embodiment of a node 700 of aneural network is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform one or more activation functions to produce its output givenone or more inputs, such as without limitation computing a binary stepfunction comparing an input to a threshold value and outputting either alogic 1 or logic 0 output or something equivalent, a linear activationfunction whereby an output is directly proportional to the input, and/ora non-linear activation function, wherein the output is not proportionalto the input. Non-linear activation functions may include, withoutlimitation, a sigmoid function of the form

${f(x)} = \frac{1}{1 - e^{- x}}$given input x, a tanh (hyperbolic

$\frac{e^{x} - e^{- x}}{e^{x} + e^{- x}},$tangent) function, of the form a tanh derivative function such as f(x)=tanh²(x), a rectified linear unit function such as f (x)=max (0, x),a “leaky” and/or “parametric” rectified linear unit function such as f(x)=max (ax, x) for some a, an exponential linear units function such as

${f(x)} = \{ \begin{matrix}{{x{for}x} \geq 0} \\{{\alpha( {e^{x} - 1} ){for}x} < 0}\end{matrix} $for some value of a (this function may be replaced and/or weighted byits own derivative in some embodiments), a softmax function such as

${f( x_{i} )} = \frac{e^{x}}{{\sum}_{i}x_{i}}$where the inputs to an instant layer are x_(i), a swish function such asf (x)=x * sigmoid(x), a Gaussian error linear unit function such asf(x)=a(1+tanh (√{square root over (2/π)}(x+bx^(r)))) for some values ofa, b, and r, and/or a scaled exponential linear unit function such as

${f(x)} = {\lambda\{ {\begin{matrix}{{\alpha( {e^{x} - 1} ){for}x} < 0} \\{{x{for}x} \geq 0}\end{matrix}.} }$Fundamentally, there is no limit to the nature of functions of inputsx_(i) that may be used as activation functions. As a non-limiting andillustrative example, node may perform a weighted sum of inputs usingweights w_(i) that are multiplied by respective inputs x_(i).Additionally or alternatively, a bias b may be added to the weighted sumof the inputs such that an offset is added to each unit in the neuralnetwork layer that is independent of the input to the layer. Theweighted sum may then be input into a function φ, which may generate oneor more outputs y. Weight w_(i) applied to an input x_(i) may indicatewhether the input is “excitatory,” indicating that it has stronginfluence on the one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y, for instance by the corresponding weight having a smallnumerical value. The values of weights w_(i) may be determined bytraining a neural network using training data, which may be performedusing any suitable process as described above.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module. As used herein,“module” may refer to a hardware module or a software module. A hardwaremodule is any collection of hardware configured to perform at least aspecified task. A software module, conversely, is any collection ofsoftware instructions configured to perform at least a specified task.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs, one or more hard disk drives in combination with a computermemory, a distributed storage system such as cloud storage, and thelike. As used herein, a machine-readable storage medium does not includetransitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module. As used herein,“module” may refer to a hardware module or a software module. A hardwaremodule is any collection of hardware configured to perform at least aspecified task. A software module, conversely, is any collection ofsoftware instructions configured to perform at least a specified task.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs, one or more hard disk drives in combination with a computermemory, a distributed storage system such as cloud storage, and thelike. As used herein, a machine-readable storage medium does not includetransitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

Now referring to FIG. 9 , an exemplary segmentation and trendlinesegment fit is illustrated. The ledger data 132 has been segmented basedon segmentation methods described herein, and four distinct segmentshave been determined, labeled segments 1-4, which have been identifiedby processor 108. Based on the identified segmentations, processor 108has fit four segment trendlines as illustrated by the dotted lines. Thefigure illustrates cumulative premium paid into a life insurance policy.Segment 1 is a segment affected by an external event and is thereforenoisy and somewhat random, for example a customer was young and paidinto a policy irregularly and somewhat impulsively withdrew money fromthe policy twice, decreasing its value. Segment 2 shows a mildexponential growth as the user consistently paid in premiums and thepremiums increased slightly with age. Segment 3 illustrates more drasticexponential growth due to a greatly increased risk of death due to oldage. In segment 4, the user has died and no longer pays premiums intothe life insurance policy.

If not sufficiently clear from contextual or plain and ordinary usage,the terms “about,” “around,” “approximately,” and “substantially,” whenused to modify a value, number, figure, quantity, or other term, can beunderstood to mean±20% of the modified value, inclusive. For instance,if not sufficiently clear from contextual or plain and ordinary usage,“about 10” can be understood to mean “from 8 to 12 inclusive”. If notsufficiently clear from contextual or plain and ordinary usage, the term“relatively” is used to indicate that one of ordinary skill in the artwould more closely associate the described value or modifier with theterm it modifies (such as high) than another term in a similar class ofwords (such as low or medium). For instance, if a temperature isdescribed as being “relatively high,” one of ordinary skill in the artwould more closely associate said temperature with “high” temperaturesthan “medium” or “low” temperatures. In another example, if a tirepressure between 30-33 psi is considered “standard,” then the term“relatively low pressure” would indicate that the stated pressure wouldbe more readily identified by one of ordinary skill in the art as being“low” than being “standard;” for instance, 26 psi.

As used herein, “and/or” is meant to include all possible permutationsof “and” and “or”. “And/or” may indicate every element of a specifiedgrouping, combinations of less than all elements, or one element. Forexample, A, B, and/or C can mean any single one of A, B, or C; A and Bbut not C, B and C but not A, A and C but not B; and A, B, and Ctogether.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for predictive ledger generation,the apparatus comprising: a processor; and a memory communicativelycoupled with the processor, the memory containing instructions storedthereon, the instructions configuring the processor to: receive a ledgerfile containing ledger data; classify the ledger file to a ledger type,wherein classifying the ledger file further comprises: receiving ledgertraining data correlating a plurality of ledger data types with aplurality of ledger classification types; training a ledgerclassification machine learning model with the ledger training data;outputting the one or more ledger classifications by: inputting theledger file into the trained ledger classification machine learningmodel; and receiving the one or more segment trendlines as outputs fromthe trained ledger classification machine learning model; and identifyone or more trends in the ledger data, wherein identifying the one ormore trends in the ledger data further comprises: segmenting the ledgerdata into one or more trend segments based on the identified one or moretrends fitting one or more segment trendlines to each of the one or moretrend segments, wherein fitting the one or more segment trendlines toeach of the one or more trend segments further comprises: receivingtrendline training data correlating a plurality of trendline types witha plurality of segment types; training a trendline fit machine learningmodel with the trendline training data; and outputting the one or moresegment trendlines by:  inputting the trend segments to the trainedtrendline fit machine learning model; and  receiving the one or moresegment trendlines as outputs; and generating at least one predictivetrendline based on the segment trendlines.
 2. The apparatus of claim 1,wherein the processor is configured to optimize the at least onepredictive trendline based on input received from a user through a userinterface.
 3. The apparatus of claim 1, wherein the processor is furtherconfigured to: identify one or more external events influencing theledger data; and segment the ledger data into one or more trend segmentsas a function of the one or more external events.
 4. The apparatus ofclaim 3, wherein the processor is further configured to minimize aneffect of the one or more external events on the ledger data.
 5. Theapparatus of claim 1, wherein the processor is further configured toselect one or more elements of the ledger data to store to theblockchain based on the ledger type.
 6. The apparatus of claim 1,wherein the processor is further configured to classify the ledger fileto a ledger type based on a pecuniary parameter.
 7. The apparatus ofclaim 6, wherein the pecuniary parameter corresponds to both of aninsurance parameter and an investment parameter.
 8. A method forpredictive ledger generation, the method comprising: receiving, by aprocessor, a ledger file containing ledger data; classifying, by theprocessor, the ledger file to a ledger type, wherein classifying theledger file to the ledger type further comprises: receiving, by theprocessor, ledger training data correlating a plurality of ledger datatypes with a plurality of ledger classification types; training, by theprocessor, a ledger classification machine learning model with theledger training data; and outputting, by the processor, the one or moreledger classifications by: inputting the ledger file into the trainedledger classification machine learning model; and receiving the one ormore segment trendlines as outputs from the trained ledgerclassification machine learning model; and analyzing, by the processor,the ledger file to identify one or more trends in the ledger data,wherein analyzing the ledger file further comprises: segmenting, by theprocessor, the ledger data into one or more trend segments based on theidentified one or more trends; fitting, by the processor, one or moresegment trendlines to each of the one or more trend segments, whereinfitting the one or more segment trendlines further comprises: receiving,by the processor, trendline training data correlating a plurality oftrendline types with a plurality of segment types; training, by theprocessor, a trendline fit machine learning model with the trendlinetraining data; and outputting, by the processor, the one or more segmenttrendlines by: inputting the trend segments to the trained trendline fitmachine learning mode; and receiving the one or more segment trendlinesas outputs from the trained trendline fit machine learning model; andgenerating, by the processor, at least one predictive trendline based onthe segment trendlines.
 9. The method of claim 8, further comprisingoptimizing, by the processor, the at least one predictive trendlinebased on input received from a user through a user interface.
 10. Themethod of claim 8, further comprising segmenting, by the processor, theledger data into one or more trend segments by identifying one or moreexternal events influencing the ledger data.
 11. The method of claim 10,further comprising minimizing, by the processor, an effect of the one ormore external events on the ledger data.
 12. The method of claim 8,further comprising selecting, by the processor, one or more elements ofthe ledger data to store to the immutable sequential listing based onthe ledger type.
 13. The method of claim 8, further comprisingclassifying, by the processor, the ledger file to a ledger type based ona pecuniary parameter.
 14. The method of claim 13, wherein the pecuniaryparameter corresponds to both of an insurance parameter and aninvestment parameter.
 15. The apparatus of claim 1, wherein fitting theone or more segment trendlines to each of the one or more trend segmentsfurther comprises: determining a plurality of coefficients for the oneor more fitted segment trendlines by iterating an optimization;generating a plurality of weighted segment trendlines with the pluralityof coefficients; training the trendline fit machine learning modeliteratively with the trendline training data and the plurality ofweighted segment trendlines.
 16. The method of claim 8, wherein fittingthe one or more segment trendlines further comprises: determining aplurality of coefficients for the one or more fitted segment trendlinesby iterating an optimization; generating a plurality of weighted segmenttrendlines with the plurality of coefficients; training the trendlinefit machine learning model iteratively with the trendline training dataand the plurality of weighted segment trendlines.
 17. The apparatus ofclaim 1, wherein the ledger file is stored to an immutable sequentiallisting as a function of the ledger type and the identified one or moretrends.
 18. The method of claim 8, wherein the ledger file is stored, bythe processor, to an immutable sequential listing as a function of theledger type and the identified one or more trends.